The second harmonic signal serves as the informational foundation for the inversion of the gas concentration in the tunable diode laser absorption spectroscopy (TDLAS) detection method. However, due to the impact of n...
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The second harmonic signal serves as the informational foundation for the inversion of the gas concentration in the tunable diode laser absorption spectroscopy (TDLAS) detection method. However, due to the impact of noise on the second harmonic signal, the accuracy and stability of the detection system are decreased. In this paper, a digital denoising method based on Long Short-Term Memory Denoising Autoencoder (LSTM-DAE) is proposed to address the issue of multiple noise interference in TDLAS detection system. In LSTM-DAE, the conventional DAE is combined with LSTM structure. The second harmonic signal has time correlation, and the LSTM structure can selectively remember the past node state, which helps the DAE reconstruct the original data. Additionally, CO 2 absorption spectrum at 2004 nm as an example, the effectiveness of this method is confirmed through simulated and real experiments. The experimental results demonstrate that LSTM-DAE, with the signal-to-noise ratio of 46.65 dB, the correlation coefficient of 0.9983, and the system detection limit of 6.1 ppb, outperforms other competitive methods in both qualitative and quantitative evaluations.
We report low-loss optical Nyquist pulse train generation using a non-auxiliary wavelength selective switch (WSS). The typical approach for optical Nyquist pulse train generation involves two procedures. The first is ...
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We report low-loss optical Nyquist pulse train generation using a non-auxiliary wavelength selective switch (WSS). The typical approach for optical Nyquist pulse train generation involves two procedures. The first is conversion from a laser output Gaussian pulse to a Nyquist pulse via spectral filtering with a WSS. The second is multiplexing to generate a Nyquist pulse train with an optical circuit. To generate a high optical signal-tonoise ratio (OSNR) Nyquist pulse train, the first procedure was improved by developing a high-power laser and improving the filtering process by using nonlinear effects in a highly nonlinear fiber. The second procedure also has the potential to further improve the OSNR, because an optical circuit typically causes an optical loss of 9 dB in the case of eight-multiplexing. In this study, we demonstrate optical loss reduction for multiplexing using a nonauxiliary WSS approach without an auxiliary optical circuit. The experimental results show that the optical loss for the Nyquist eight-pulse train is successfully reduced to 2.8 dB.
Drivable terrain detection is a significant feature of study receiving attention from many researchers because of its contribution to successful navigation of autonomous drivable system. However, various vision-based ...
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Drivable terrain detection is a significant feature of study receiving attention from many researchers because of its contribution to successful navigation of autonomous drivable system. However, various vision-based techniques for drivable terrain detection with incredible result exist. Irrespective of these successes, light intensity is an environmental noise that makes their system fail because it has the capability to cause erroneous detection of drivable terrain. In this paper, after a brief examination of light intensity effect, we optimized by introducing a filtering algorithm into the drivable terrain detection system to address the problem of light intensity during the day. Experimental performance of the optimized system was tested qualitatively. Quantitative comparison based on accuracy rate (ACC), error rate (ERR), total positive rate (TPR), false negative rate (FNR), total negative rate (TNR), false positive rate (FPR), and precision (PRE) are used to test the system. The result of the proposed system shows a better performance and has improved enormously the navigation of the autonomous driving system.
Noise is the information damage that may occur in the image due to the changes in information during the transmission process. In order to overcome these problems, it is necessary to do filtering process on the image....
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Noise is the information damage that may occur in the image due to the changes in information during the transmission process. In order to overcome these problems, it is necessary to do filtering process on the image. Until now many filtering algorithms have been proposed to remove noise. Most existing methods only work for low level noise. In this study, the authors proposed an efficient and easy-to-understand filtering algorithm using the concept of tropical algebra and singular value decomposition (SVD). The SVD will be used to detect noise in 3 x 3 templates. Furthermore, if noise is detected then new pixels will be obtained by using the concept of tropical operations. The results of this study show that the proposed method provides better results from the existing methods in terms of quantitative and visual.
The rapid adoption of wireless technologies has increased the interest of many laboratories about the field of wireless sensor network or the radio-frequency identification (RFID) technology which has emerged as a win...
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The rapid adoption of wireless technologies has increased the interest of many laboratories about the field of wireless sensor network or the radio-frequency identification (RFID) technology which has emerged as a winning combination for the implementation of an advanced assistance system within smart environments. To fulfill the important mission of a technological assistance, a technique first had to identify the ongoing activities of its user by tracking everyday life objects in real time using, for example, passive RFID tags. To increase the quality of information extracted from the objects localization by properly using the received signal strength indicator, this paper explores Kalman filter, particle filter and few others filtering algorithm that enhances the tracking performance. It also discusses three of the most interesting methods that can be applied for the localization of objects in smart environments without requiring the installation of references tags everywhere. Finally, to increase the value, we include experiments that were conducted within a real smart home infrastructure to review the positive and negative elements of each method.
The AtMostSeqCard constraint is the conjunction of a cardinality constraint on a sequence of n variables and of n -aEuro parts per thousand q + 1 constraints AtMost u on each subsequence of size q. This constraint is ...
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The AtMostSeqCard constraint is the conjunction of a cardinality constraint on a sequence of n variables and of n -aEuro parts per thousand q + 1 constraints AtMost u on each subsequence of size q. This constraint is useful in car-sequencing and crew-rostering problems. In van Hoeve et al. (Constraints 14(2):273-292, 2009), two algorithms designed for the AmongSeq constraint were adapted to this constraint with an O(2 (q) n) and O(n (3)) worst case time complexity, respectively. In Maher et al. (2008), another algorithm similarly adaptable to filter the AtMostSeqCard constraint with a time complexity of O(n (2)) was proposed. In this paper, we introduce an algorithm for achieving arc consistency on the AtMostSeqCard constraint with an O(n) (hence optimal) worst case time complexity. Next, we show that this algorithm can be easily modified to achieve arc consistency on some extensions of this constraint. In particular, the conjunction of a set of m AtMostSeqCard constraints sharing the same scope can be filtered in O(nm). We then empirically study the efficiency of our propagator on instances of the car-sequencing and crew-rostering problems.
This paper discusses electrical fatigue response for ferroelectric ceramics under electrical cyclic load. The change of remanent polarization (P-r) is chosen to show the damage evolution of electrical characters of fe...
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This paper discusses electrical fatigue response for ferroelectric ceramics under electrical cyclic load. The change of remanent polarization (P-r) is chosen to show the damage evolution of electrical characters of ferroelectric ceramics PZT-5. Raw relations between P-r and number of cyclic electric load are obtained from raw loops experimentally under three applied electrical field: 431sin(100 pit), 647sin(100 pit) and 862sin(100 pit) KV/m. Exact relations between P-r and number of cyclic electric load are derived from corrected loops by applying a filter algorithm on the raw loops. The following results are obtained from experiments and error elimination analysis: P-r of ferroelectric ceramics PZT-5 decreases with the number of cycles of the applied electrical cyclic field. The higher the applied electrical field, the faster the reduction of P-r. The higher applied electrical cyclic field causes heavier damage evolution in ferroelectric material. (C) 2000 Elsevier Science Ltd. All rights reserved.
Existing self-supervised sequential recommendations face the problem of noisy interactions and sparse sequence data, and train models based only on item prediction losses, so they usually fail to learn an appropriate ...
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Existing self-supervised sequential recommendations face the problem of noisy interactions and sparse sequence data, and train models based only on item prediction losses, so they usually fail to learn an appropriate sequential representation. In this paper, to address the above problem, we propose long and short-term interest contrastive learning under filter-enhanced sequential recommendation (FLSCSR). Specifically, a filtering algorithm is used on the user's interaction sequences to attenuate the noisy information in the sequence data. Two independent encoders are used to model the user's long-term and short-term interests separately on the filter-based enhanced interaction sequences. Then user-specific gating mechanisms are constructed to capture the long-term and short-term interests tailored to the user's personalized preferences, which are incorporated into the attention network to achieve better learning of interest representations in sequence recommendations. In addition, representation alignment learning goals are proposed to minimize the discrepancy between long-term and short-term interest representations in personalized global contexts and local sequence representations. Experiments were conducted on three public and industrial datasets, where the FLSCSR model could obtain superior performance compared to the benchmark model: AUC improves by 0.76%-2.02%, GAUC improves by 0.55%-1.01%, MRR improves by 1.19%-2.09%, and NDCG@2 improves by 1.07%-2.26%.
To overcome the difficulty of collecting fine-grained terrain data that is important for flood modelling, this work presents a method for the application of UAV-based LiDAR techniques to drive high-resolution flood pr...
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To overcome the difficulty of collecting fine-grained terrain data that is important for flood modelling, this work presents a method for the application of UAV-based LiDAR techniques to drive high-resolution flood propagation and inundation modelling. This paper comprehensively introduces the UAV platform, LiDAR sensor and data processing techniques required and proposes the approach for obtaining refined DEM for flood management using which the DEM accuracy can reach +/- 3 cm. Accordingly, two kinds of terrains, a small mountain area and a large urban area, have been measured and the time requirements for the method are 5 h and 2 days respectively. Based on the collected data, a full hydrodynamic numerical flood model is applied to simulate a flash flood in the mountain catchment and an urban flood at high-resolution. The results show that the water depth and velocity affected by key micro terrain features, such as tiny channels and roads, can be captured and considered, indicating that LiDAR UAV techniques are an efficient and reliable method for surveying terrain making them highly important for creating high accurate flood simulation.
This study aims to investigate autonomous location and environment mapping of moving objects under conditions of dust and weak illumination in underground tunnels. The standard extended Kalman filter (EKF) algorithm h...
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This study aims to investigate autonomous location and environment mapping of moving objects under conditions of dust and weak illumination in underground tunnels. The standard extended Kalman filter (EKF) algorithm has a issue that system noise and the prior statistical characteristics of the observed noise cannot be accurately predicted. Thus, we propose an improved EKF algorithm to perform fuzzy adaptive simultaneous localization and mapping (SLAM). Laser matching is added to EKF prediction phase to predict the position, and the weighted average position is used as the final position of the predicted part. By observing the change of the mean value and covariance, the system noise and the weighted value of the observed noise are fuzzy adjusted. The improved filtering algorithm is applied to a SLAM simulation experiment, and the influence of four different landmark arrangements on position estimation is considered. The results show that the positioning and composition accuracy can be improved using the new algorithm, and the accuracy of positioning and composition is increased by more than 53.8% compared with standard EKF in y-direction. It is also found that a landmark layout along the center line of a roadway roof is superior to other arrangement methods.
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